An introduction to Conflict-Free Replicated Data Types

Part 4: Combinators

Previous part Next part

This is a series about Conflict-Free Replicated Data Types, or CRDTs for short. Their purpose is to allow seamless replication of data on different nodes in a distributed system. Merging is by construction always possible, without any conflicts. This series assumes no knowledge about CRDTs, but be prepared to learn a thing or two about algebras. All code samples on this page are interactive and executed in your browser. Understanding the code is necessary for understanding the concepts, so you should be familiar with JavaScript. If you notice any bugs on this page, please let me know!

Avid Factorio players know: It's faster if you use tools to create tools.

What’s happening in this episode

I ended the last episode with a bit of a cliffhanger, by claiming that there is a way to compose small CRDTs into larger CRDTs. I’d like to tackle this today. Since I like playing Factorio, I’m going to use this video game as an analogy for describing combinators. If you already know what these are, you can safely skip over most of this post.

To all others, please don’t type “combinators” into Wikipedia. You’ll end up reading about combinatory logic which is cool and topical, but unfortunately entirely unhelpful.

About composition

A popular misunderstanding is that functional programming is all about functions. In reality, functional programming is also all about composing those functions. After all, just a bunch of unrelated functions aren’t really cool.

Factorio is a game where you start out wringing resources with your bare hands from the environment and end up designing intricate supply chains for high-tech factories. It is really a lot like programming. Why? Because the goal of the game – building a rocket – simply can’t be achieved by doing everything manually. You’ll need automation to scale up mining and production.

To reach this goal, Factorio offers various meta-tools. For example, you can build a factory (the game calls them assembling machines) that takes in ingredients and produces whatever the recipe says. While in principle, a player can also execute that recipe, this quickly becomes unwieldy. Instead, you’d manufacture a bunch of assembling machines manually that then do the manufacturing for you.

See the similarity to programming? Instead of doing every little thing by hand (using assembly), we build abstractions that can build on other, lower-level abstractions. In this article, we already use many layers of these abstractions, with the interactive JavaScript snippets sitting at the top.

As it turns out, I’ve already snuck in a few combinators in the past episodes, but without explaining them. fast-check also allows us to take basic generators and compose them into larger generators. The following example shows that in action: we have a basic generator (“hexadecimal string up to length 5”) and feed it into a combinator (“an array up to length 3”).

  fc.array(fc.hexaString(5), 3)

Cool, huh? In this example, fc.array is a combinator, because it transforms a generator for elements into a generator for arrays of those elements. How it does that is entirely irrelevant; what matters is that it provides us with a nice and clean abstraction boundary.

Of course, it is possible to nest this:

  fc.array(fc.array(fc.hexaString(5), 2), 2)

This is similar in Factorio. You can set up a factory that takes in some base resources and produces some kind of product. Naturally, a factory itself can also be produced by another factory, since it is just made out of some base resources. A factory doesn’t really care what it’s producing, as long as it’s got the recipe and sufficiently many resources. And it’s guaranteed that the output will always be of the specified type. No exceptions (pun intended)! Unless you have no resources, in which case the factory grinds to a halt. This would be comparable to an out-of-memory-error in programming.

About abstractions

But not all abstractions are created equal. There’s much talk in programming about abstractions that are leaky, ill-defined, introduce performance penalties, or … This is not what I’m going to talk about here. You may have guessed it: We’ll focus on mathematical abstractions. Those have a clear definition and provably satisfy a set of properties.

In fast-check, the guarantee is driven by the types. We can’t see them in JavaScript, but they’re there. A generator will always produce a sequence of objects that are of a particular type. For example, the fc.array combinator always produces arrays.

Other mathematical abstractions we’ve seen so far are partial orderings and lattices. Their contracts specify precisely what you can expect them to do.

We can take all of this to the next level by talking about instantiating an abstraction for data structures that are themselves abstract. For example, we can look at how to implement a lattice for a Map with arbitrary key and value types.

As a concrete example, recall the distributed counter from the previous episode. To rehash: every participant keeps a key-value map, mapping a unique participant identifier to a positive integer. To increment the counter, a participant increases their own integer. Merging happens by looking at two maps and taking the maximum for each key.

The data structure involved here is, in TypeScript notation, a Map<string, number>. This is a concrete type because we know all involved types: Map, string and number.

Let’s try to implement the lattice for this concrete type first, and then think about generalizing it. The following snippet will do three things:

  1. Define a merging operation for maps
  2. Check it on a concrete example
  3. Check the lattice contract
function maxNumber(x, y) {
  if (x <= y)
    return y;
  return x;

function mergeMaps(map1, map2) {
  const result = new Map(map1.entries());
  for (const [key, value] of map2.entries()) {
    if (result.has(key))
      result.set(key, maxNumber(result.get(key), value));
      result.set(key, value);
  return result;

    new Map([["alice", 1], ["bob", 0], ["claire", 2]]),
    new Map([["alice", 0], ["bob", 1], ["dave", 4]])
  new Map([["alice", 1], ["bob", 1], ["claire", 2], ["dave", 4]])

const smallStringGen = fc.hexaString(3);
const smallMapGen = fc.set(fc.tuple(smallStringGen, fc.nat(5)), 5).map(elems => new Map(elems));

checkAll(contracts.lattice({ join: mergeMaps }, smallMapGen));

That appears to have worked.

Into the abstract

Concrete types are pretty much worthless

Now look very carefully at the existing implementation. What parts are specific to strings and numbers?

The answer is: none of them. Here’s the exhaustive list of operations we perform on the keys and values:

  1. we compute the maximum of two values

That’s it. That’s the entire story.

To compute the join of two maps, we merely need to know how to compute the join of the values. We only look at the keys for checking if they’re present in both maps, but otherwise, we have to know nothing.

Taken further, this means that the concrete types in the map serve no purpose. They are entirely useless. We only need a tiny bit of structure on the values (namely, their lattice) to build the lattice for the whole map.

This has a ton of advantages. Unfortunately, they’re invisible in JavaScript. In a type system, we could deduce a great deal of what’s going on inside functions just by looking at their types. But I digress.1

Let’s finally take a look at the generic lattice implementation for maps. As usual, I’m monkey-patching the merge function onto the Map prototype:

Map.prototype.merge = function (that, valueMerger) {
  const result = new Map(this.entries());
  for (const [key, value] of that.entries()) {
    if (result.has(key))
      result.set(key, valueMerger(result.get(key), value));
      result.set(key, value);
  return result;

const map1 = new Map([["alice", 1], ["bob", 0], ["claire", 2]]);

const map2 = new Map([["alice", 0], ["bob", 1], ["dave", 4]]);

const merged = map1.merge(map2, (x, y) => x >= y ? x : y);

  new Map([["alice", 1], ["bob", 1], ["claire", 2], ["dave", 4]])

This is pretty much what we had before, but additionally, we have to specify a function for merging the values of the map. Finally, we’re left to define the lattice: = valueLattice => ({
  join: (map1, map2) => map1.merge(map2, valueLattice.join)

lattices.any = {
  join: (x, y) => x >= y ? x : y

I’ve also added an any lattice that’s just based on <= for JavaScript built-ins, like numbers.

Let’s check the contract:

const smallStringGen = fc.hexaString(3);
const smallMapGen = fc.set(fc.tuple(smallStringGen, fc.nat(5)), 5).map(elems => new Map(elems));

  contracts.lattice(, smallMapGen)

Nothing in our definitions above is specific to any concrete type. We can reuse this code for other purposes and types. I have one more mind-blowing concrete example for this, but before we go into that, let’s revisit those G-Counters from the last episode.

Grow-only counters revisited

I’ve already told you that a G-Counter is a CRDT. But why is that?

In their 2011 paper, Shapiro et al. define a particular kind of CRDTs, so-called State-based CRDTs, as data types that:

  1. have a join-semilattice
  2. only support monotonic operations

We’ve already seen both components in isolation, and G-Counter shows their interaction.

Let’s first see the lattice. It can be constructed as follows:

This is a structure that needs to be known by the system infrastructure. When two nodes communicate and update their respective states, they use the join operation from the lattice to merge them together.

But this only works nicely if the application developers only manipulate the state in a monotonic fashion. For G-Counters, it means that nobody ever decreases a counter.

Formally speaking, the guarantee that would be violated in case of a non-monotonic update is convergence. The paper explains this in very clear terms:

Any two object replicas of a CvRDT [State-based CRDT] eventually converge, assuming the system transmits payload infinitely often between pairs of replicas over eventually-reliable point-to-point channels.

Meaning: you play by the rules, you get convergence.

Sets are just maps without extra junk

Now for the blown minds I’ve promised you. Remember how we defined the lattice for sets earlier:

  lattices.set.join(set(1, 2, 3), set(1, 4)),
  set(1, 2, 3, 4)

Imagine for a second that JavaScript didn’t have a built-in Set type. How would you present a set of things? Your representation should be canonical, i.e. there shouldn’t be two different ways to represent the same set. Otherwise, we could just use arrays and pretend that duplicate elements don’t exist.

Got an idea? Cool, feel free to implement it down below:

// free space

Of course, the section heading gave it away already. Sets can be easily represented by maps. In TypeScript terms, Set<T> is equivalent (or isomorphic) to Map<T, void>. In other words, a set is the same thing as a map where all values are undefined.

Curiously enough, the JavaScript standard library already acknowledges this equivalence by using the same method names in sets and maps:

  new Set([1, 2, 3]).has(2)

  new Map([[1, undefined], [2, undefined], [3, undefined]]).has(2)

This equivalence also extends to the various algebraic structures we’ve seen. The lattice implementation for sets can be derived directly from maps:

lattices.void = {
  join: () => undefined

const alternativeSetLattice =;

This weird lattices.void instance is what the mathematicians call trivial because it only deals with exactly one value: undefined. You can only call join on undefined and the result is undefined again. But that’s exactly what we want here.

And what about order?

Eagle-eyed readers may have observed the lack of partial ordering for maps in this episode. It’s true, I haven’t shown you this so far. So, for completeness’ sake, the solution is below: = valueOrdering => ({
  isLeq: (map1, map2) => {
    for (const [k, v1] of map1.entries()) {
      if (map2.has(k)) {
        const v2 = map2.get(k);
        if (!valueOrdering.isLeq(v1, v2))
          return false;
      else {
        return false;
    return true;

const smallStringGen = fc.hexaString(3);
const smallMapGen = fc.set(fc.tuple(smallStringGen, fc.nat(5)), 5).map(elems => new Map(elems));


It works in exactly the way as the partial ordering for sets. For one map to be smaller than another map, the other map needs to have at least the same keys defined. Then, for each value, we need to check that the other map’s value is larger or equal. Feel free to try it out on some examples!

Now, just as above, we can define the trivial partial ordering and derive the set partial ordering:

orderings.void = {
  isLeq: () => true // always true since undefined == undefined

const alternativeSetOrdering =;

Feel free to use the code boxes to convince yourself that this construction is indeed equivalent to the one I defined two episodes ago.

In reality, the situation is slightly more complicated though, because a Set is, in JavaScript terms, not even deeply equal to a Map:

// sadness 😢
// also pls ignore the terrible assertion message
assert.deepEqual(new Set([1]), new Map([[1, undefined]]));

In order to make our alternative algebras work with actual JavaScript Sets, we’d need to convert between Sets and Maps before and after invoking the join (or isLeq) method.

But clever CRDT implementations may instead go ahead and define both G-Sets and G-Counters based on the very same map data structure. Poof, lots of code duplication gone!

However note that they’d probably not expose their underlying maps, because they need to prevent those pesky users from performing non-monotonic operations. For G-Sets, that’d be deleting elements, and for G-Counters decreasing values.

What’s next?

Says the programmer to the mathematician: I find your line of work to be too monotone.

Replies the mathematician: You’re right. But at least it’s continuous and unbounded.

I’ve spent four episodes and well above 7000 words to explain liek two different CRDTs. (You know how it goes: one day you wonder how babby algebra is formed, and then you accidentally a blog.) But fear not, for now we have all the tools we need to proceed to more complex CRDTs. In the next episode, we’re going to look at how to deal with deletion. Spoiler: prepare for tombstones 👻


  1. I’ve written elsewhere about this subject. Unfortunately, this is in German, but by typing “parametricity” into your favourite search engine, you may find some further material. 

Thanks to the people who've read drafts of this series and provided valuable feedback: Andrea, Clement Delafargue, Heiko Seeberger, Hillel Wayne, Johannes Link, Matthew Weidner, Princess.